搜索资源列表
SVM
- 支持向量机用于训练和分类，包括PCA降维等函数-Support vector machines for training and classification, including functions such as PCA dimension reduction
face
- Matlab PCA+SVM人脸识别,通过PCA和SVM算法达到人脸识别的功能。-Matlab PCA+SVM，To identify people s face.
PCA-BEL(ToolBox1.0)
- 又提供一基于生理的情感神经网络，包含杏仁体，丘脑，视觉神经等重要元素，可用于分类与预测，性能优越于SVM，BP-The use of neural network based on emotional physiology, contains important elements of the amygdala, thalamus, optic nerve, etc., can be used for classification and prediction, superior perform
SVM
- 支持向量机SVM和核函数的MATLAB程序集,用于图形处理的算法，分类算法-pca and svm use MATLAB
face-gabor-pca
- 基于gabor的人脸识别 带有pca降维 最后用svm识别-Finally, svm pca dimensionality reduction recognition with face recognition based on gabor
Classification-MatLab-Toolbox
- 模式分类工具箱，有PCA、SVM、ID3源代码，用于数据分析、模式识别和机器视觉。-Pattern classification toolbox, there PCA, SVM, ID3 source code for data analysis, pattern recognition and machine vision.
code-faceGUI.m
- 图形界面的人脸识别代码，PCA+SVM多分类，yale图片库-Face recognition codes in GUI, PCA+SVM multi-classification, yale gallery
pca_svm
- PCA+svm算法进行人脸识别，识别率在百分之80~90- Face recognition algorithm Pca+ support vector machine Recognition rate of about ninety percent, interested friends can be used as a reference
SVM--ICA-and-PCA-and-NN
- SVM,ICA,PCA,NN等等模式识别算法,很有参考-SVM, ICA and PCA and NN, and so on pattern recognition algorithm, is of great reference value
FaceRec_SourceCode
- 基于PCA-SVM的人脸识别，平均识别率达83 ，是基于matlab开发的。-PCA-SVM-based face recognition, the average recognition rate of 83 , based on matlab development.
classification_toolbox
- 多种用于分类的matlab代码，包含PCA,SVM,PLS-DA，KNN,SOMF等.-For various categories of matlab code, and contains the PCA and SVM, PLS- DA, KNN, SOMF, etc.
machine-learning-ex2-8
- 斯坦福机器学习网上公开课相关编程练习代码，包括线性回归，逻辑回归，神经网络，PCA,SVM等。-the programming code of online course Mechine Learning provided by Stanford.
PCA_SVM face recognition
- relize the idea of the face recognition of PCA-SVM
FaceRec
- 人脸表情识别matlab程序PCA+SVM算法，SVM分类-orL人脸数据库有数据有图片-Facial expression recognition matlab program PCA+SVM algorithm, SVM classification-orL face
FaceRec
- 人脸识别（PCA+SVM） 文件中包含训练样本，运行后，能进行人脸识别，采用PCA进行降维，利用ＳＶＭ 进行分类识别-Face recognition（PCA+SVM）
Attribute profiles
- 选择合适的样本特征点，然后可以将特征导入svm进行分类(After the image processing, the main information is obtained by PCA transform, and then the feature of texture information selection is put forward)
SVM-KMExample
- examples of SVM, PCA , MultiSVM, Feature extraction, kernel function
基于主分量的人脸重构
- 本实验是基于主成分分析法（PCA）在人脸识别中的应用，采用SVM分类器在ORL人脸库的基础上通过Matlab实现了快速PCA算法的验证仿真。
(PCA+SVM)人脸识别
- 人脸识别，降维 加分类，主成分分析降维，支持向量机分类(Face recognition, principal component analysis reduced Vega classification, dimension reduction, support vector machine classification)
face-Adaboost
- 用Adaboost和PCA算法实现人脸识别，用Python写的代码，根据经典的PCA和SVM算法改编(Adaboost and PCA algorithm for face recognition, code written in Python, adapted from the classic PCA and SVM algorithm)